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Case-based Reasoning - learning through experience

Case-based Reasoning is one of the most successful applied AI technologies of recent years. Commercial and industrial applications can be developed rapidly, and existing corporate databases can be used as knowledge sources. Helpdesks and diagnostic systems are the most common applications.

Case-based Reasoning (CBR) is based on the intuition that new problems are often similar to previously encountered problems and, therefore, that past solutions may be of use in the current situation. Cases are often derived from legacy databases, thereby converting existing organisational resources into exploitable knowledge. CBR is particularly applicable to problems where the domain is not understood well enough for a robust statistical model or system of equations to be formulated. CBR is commonly used for diagnosis (or, more generally, for classification tasks), e.g., to determine a fault from observed attributes, or to determine whether or not cancer treatment is necessary given a set of past cases. AIAI has applied case-based reasoning to otherwise intractable problems such as fraud screening.

While the case-based reasoning methodology can be applied consistently across application domains, the implementation of the retrieval and similarity scoring functions is typically highly customised to the problem at hand. Two factors become critical: The availability of a flexible CBR Shell, and the accumulated practical experience of applying artificial intelligence techniques to real-world problems.
AIAI, has a Case-based Reasoning Shell whose design has been developed and refined over numerous commercial projects. This tool has an advanced performance optimisation module, based on a genetic algorithm, and both Java and C++ versions have been implemented.

Fraud Detection

At the request of one of the UK's most successful fraud detection system software providers, AIAI undertook an investigation into methods of applying new AI technologies to increase the accuracy of the already highly advanced systems presently in use. While the firm's software presently reduces the number of necessary fraud investigations by several orders of magnitude, our investigation showed that utilising adaptive algorithms and fuzzy logic results in significant diagnostic improvement on the most difficult sub-section of cases. The goal of the work was to reduce the number of applications referred for costly manual investigation after the existing detection systems had been utilised. The developed CBR system was able to prioritise the referred applications from the most to the least suspicious, aiding the decision process of the fraud investigator.

Projects Publications Contact
Fraud Detection
CBR Applet Demo
Multiple Algorithms for Fraud Detection
CBR Shell - Click-Thru Licensing
Stuart Aitken

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Updated: Tue Feb 8 10:07:32 2011
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